class_docstr: Base class for Item-Item Nearest Neighbour recommender models
  here.
  
  Parameters
  ----------
  K : int, optional
      The number of neighbours to include when calculating the item-item
      similarity matrix
  num_threads : int, optional
      The number of threads to use for fitting the model. Specifying 0
      means to default to the number of cores on the machine.
code: def recommend(
      self,
      userid,
      user_items,
      N=10,
      filter_already_liked_items=True,
      filter_items=None,
      recalculate_user=False,
      items=None,
  ):
      if not isinstance(user_items, csr_matrix):
          raise ValueError("user_items needs to be a CSR sparse matrix")
  
      if not np.isscalar(userid):
          if user_items.shape[0] != len(userid):
              raise ValueError(
                  "user_items must contain 1 row for every user in userids"
              )
  
          return _batch_call(
              self.recommend,
              userid,
              user_items=user_items,
              N=N,
              filter_already_liked_items=filter_already_liked_items,
              filter_items=filter_items,
              recalculate_user=recalculate_user,
              items=items,
          )
  
      if filter_items is not None and items is not None:
          raise ValueError("Can't specify both filter_items and items")
  
      if filter_items is not None:
          N += len(filter_items)
      elif items is not None:
          items = np.array(items)
          N = self.similarity.shape[0]
          # check if items contains itemids that are not in the model(user_items)
          if items.max() >= N or items.min() < 0:
              raise IndexError(
                  "Some of selected itemids are not in the model"
              )
  
      ids, scores = self.scorer.recommend(
          user_items.indptr,
          user_items.indices,
          user_items.data,
          K=N,
          remove_own_likes=filter_already_liked_items,
      )
  
      if filter_items is not None:
          mask = np.in1d(ids, filter_items, invert=True)
          ids, scores = ids[mask][:N], scores[mask][:N]
  
      elif items is not None:
          mask = np.in1d(ids, items)
          ids, scores = ids[mask], scores[mask]
  
          # returned items should be equal to input selected items
          missing = items[np.in1d(items, ids, invert=True)]
          if missing.size:
              ids = np.append(ids, missing)
              scores = np.append(
                  scores,
                  np.full(missing.size, -np.finfo(scores.dtype).max),
              )
  
      return ids, scores
  
imports: import numpy
  from numpy import bincount, log, log1p, sqrt
  from scipy.sparse import coo_matrix, csr_matrix
  from ._nearest_neighbours import NearestNeighboursScorer, all_pairs_knn
  from .recommender_base import RecommenderBase
  from .utils import _batch_call
label_desc: Use image node_id 3 for calling the ItemItemRecommender obj's underlying member method code with example usage: obj.recommend(userid, user_items, N, filter_already_liked_items, filter_items, recalculate_user, items) and returns: ids,  scores, _batch_call